4.4k citations · 24.2k across the 344 of their papers we have counts for
8 papers · 2 filters
Variance Reduced methods for Non-convex Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
This paper explores the non-convex composition optimization in the form including inner and outer finite-sum functions with a large number of component functions. This problem aris…
Learning with Biased Complementary Labels
Xiyu Yu, Tongliang Liu, Mingming Gong +1
In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that o…
Duality-free Methods for Stochastic Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
We consider the composition optimization with two expected-value functions in the form of ,…
Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao +1
Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied. In this paper, we focus on Bounded Instance- and Label-dependent l…
Towards Evolutional Compression
Yunhe Wang, Chang Xu, Jiayan Qiu +2
Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly reco…
Transfer Learning with Label Noise
Xiyu Yu, Tongliang Liu, Mingming Gong +3
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain. To reduce the distribution shift between source and t…